School Districts’ Assessment of the French Language Proficiency of Prospective FSL Teachers
Bibliographic record
Abstract
It is a widespread practice among Canadian school districts to assess the French language proficiency of candidates applying for French as a Second Language (FSL) teaching positions. This pan-Canadian study used surveys, interviews, and website data to examine the French language proficiency assessment practices and processes used by Canadian schools when hiring FSL teachers. The findings show that almost 90% of Canadian school districts assess French language proficiency when hiring FSL teachers and that the most common form of assessment is to ask one to three questions in French (with candidates responding in French) during the employment interview. Evaluation of the candidates’ responses is rarely informed by a language proficiency framework or rubric; instead, evaluators’ decisions are informed by their overall impressions of the candidate’s language proficiency. Other promising assessment practices are described, along with suggestions about how districts may improve their French language proficiency assessments when hiring FSL teachers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".